Office of the Dean of the Graduate School

Note: Teaching assignments are dependent on course enrollments, candidate qualifications, and the availability of resources that must be approved by the Office of the Dean of the Faculty.

Computer Science

COS 343 — Algorithms for Computational Biology

This course introduces algorithms for analyzing DNA, RNA, and protein, the three fundamental molecules in the cell. Students will learn algorithms on strings, trees, and graphs and their applications in: sequence comparison and alignment; molecular evolution and comparative genomics; DNA sequencing and assembly; recognition of genes and regulatory elements; and RNA structure and protein interaction networks. Students will also implement algorithms and apply them to biological data.

Computer Science

COS 126 — Computer Science: An Interdisciplinary Approach

An introduction to computer science in the context of scientific, engineering, and commercial applications. The goal of the course is to teach basic principles and practical issues, while at the same time preparing students to use computers effectively for applications in computer science, physics, biology, chemistry, engineering, and other disciplines. Topics include: hardware and software systems; programming in Java; algorithms and data structures; fundamental principles of computation; and scientific computing, including simulation, optimization, and data analysis. Video lectures, one or two classes, two preceptorials.

Computer Science

COS 511 — Theoretical Machine Learning

Introduction to theoretical machine learning, including mathematical models of machine learning, and the design and rigorous analysis of learning algorithms. Likely topics include: bounds on the number of random examples needed to learn; learning from non-random examples in the on-line learning model; how to boost the accuracy of a weak learning algorithm; support-vector machines; maximum-entropy modeling; portfolio selection; game theory. Regular problem sets and final project. Attendance expected at all lectures.

Computer Science

COS 343 — Algorithms for Computational Biology

This course introduces algorithms for analyzing DNA, RNA, and protein, the three fundamental molecules in the cell. Students will learn algorithms on strings, trees, and graphs and their applications in: sequence comparison and alignment; molecular evolution and comparative genomics; DNA sequencing and assembly; recognition of genes and regulatory elements; and RNA structure and protein interaction networks. Students will also implement algorithms and apply them to biological data.

Computer Science

COS 511 — Theoretical Machine Learning

Introduction to theoretical machine learning, including mathematical models of machine learning, and the design and rigorous analysis of learning algorithms. Likely topics include: bounds on the number of random examples needed to learn; learning from non-random examples in the on-line learning model; how to boost the accuracy of a weak learning algorithm; support-vector machines; maximum-entropy modeling; portfolio selection; game theory. Regular problem sets and final project. Attendance expected at all lectures.

Computer Science

COS 463 — Wireless Networks

This course covers the design and implementation of wireless networks, from signals to bits to datagrams. Students will gain an understanding of the principles and techniques behind the design of modern wireless local-area and wide-area networks, as well as their interaction with the design of the rest of the Internet. The class will provide an introduction to the wireless physical layer, presented in a way that is accessible for students with solely a computer systems and networking background.

Computer Science

COS 433 — Cryptography

An introduction to the theory and practice of modern cryptography, with an emphasis on the fundamental ideas. Topics covered include private key and public key encryption schemes, digital signatures, pseudorandom generators and functions, chosen ciphertext security, and some advanced topics.

Engineering

EGR 201 — Foundations of Entrepreneurship

Entrepreneurship is about creating value for the benefit of others. It is about innovating, marshalling limited resources, inspiring teams, and persisting through challenges and uncertainty, often by trying, learning from what happens, and trying something better. EGR 201 is designed to equip each student with the approaches and concepts of entrepreneurship and how to apply them to achieve positive change in whatever endeavors they choose to pursue. The course employs several experiential methods of teaching and learning so students will understand how they can follow an entrepreneurial path in organizations and in life.

Engineering

EGR 498 — Special Topics in Social Entrepreneurship: Rethinking Social Profit Organizations

A growing number of entrepreneurs are solving social and environmental challenges by creating private 'nonprofit' organizations and projects. This course will explore the challenges and opportunities they face. While the course will cover the styles and competencies that successful nonprofit managers tend to exhibit, it will explore system-wide changes needed to improve the sector's outcomes, including key ways that funders, government, businesses and the beneficiaries of nonprofits can help.

Engineering

EGR 381 — Design for Understanding

Clarity and understanding are essential to creating change and impact in society, business, and everyday life. This class introduces students to processes and methods for tackling complex communication challenges across a range of contexts. We will study the theory and practice of information design, bridging cognitive factors with visual principles for effective presentation of data, concepts, and other types of content. Through hands-on assignments, we will practice methods and explore techniques to design information effectively.

Molecular Biology

MOL 558 — Psychopharmacology

The molecular biology and biochemistry of pharmaceuticals and natural products that target CNS function are examined. Specific topics include: the blood-brain barrier, addiction and tolerance, analgesia, treatments for mood disorders, cognitive enhancement, stimulants and ADHD, treatment of dementias such as Alzheimer's and Parkinson's Disease, psychotropic drugs, antipsychotics and the treatment of schizophrenia.

Department of Operations Research and Financial Engineering

ORF 401 — Electronic Commerce

Electronic commerce, traditionally the buying and selling of goods using electronic technologies, extends to essentially all facets of human interaction when extended to services, particularly information. The course focuses on both the software and the hardware aspects of traditional aspects as well as the broader aspects of the creation, dissemination and human consumption electronic services. Covered will be the physical, financial and social aspects of these technologies

Position 1 Primary Duties: Full AI position which requires 20 hours per week

Number of AI Hours with Position: 20

Background Required: Coding is required

Department of Operations Research and Financial Engineering

ORF 407 — Fundamentals of Queueing Theory

This is an introduction to the stochastic models inspired by the dynamics of resource sharing. Topics discussed include: early motivating communication systems (telephone and computer networks); modern applications (call centers, healthcare operations, and urban planning for smart cities); and key formulas (from Erlang blocking and delay to Little's law). We also review supporting stochastic theories like equilibrium Markov chains along with Markov, Poisson and renewal processes.

Position 1 Primary Duties: This is a half AI assignment which requires 10 hours per week

Number of AI Hours with Position: 10

Background Required:

Woodrow Wilson School of Public and International Affairs

WWS 351 — Information Technology and Public Policy

New technologies have changed the way we communicate with each other and learn about our world. They have also raised public policy dilemmas in every area they touch: communications, regulation, privacy, national security, intellectual property and many others. This course is predicated on the belief that we can only productively address the social and policy dimensions of the Internet if we understand the technology behind the Internet; the social-science concepts and research that illuminate the likely effects of policy options; and tradeoffs among fundamental values that different policy options imply.